“‘GAN’ Tag”,2019-10-01 (ML dataset, CLIP samples, autoencoder NN, MARL, robotics)⁠:

Bibliography for tag ai/nn/gan, most recent first: 8 related tags, 229 annotations, & 41 links (parent).

[essay on this tag topic]

• See Also
• Gwern
  • “Research Ideas”, Gwern2017
  • “GANs Didn’t Fail, They Were Abandoned”, Gwern2022
• Links
  • “MaskBit: Embedding-Free Image Generation via Bit Tokens”, Weber et al 2024
  • “SF-V: Single Forward Video Generation Model”, Zhang et al 2024
  • “VideoGigaGAN: Towards Detail-Rich Video Super-Resolution”, Xu et al 2024
  • “A Study in Dataset Pruning for Image Super-Resolution”, Moser et al 2024
  • “Hierarchical Feature Warping and Blending for Talking Head Animation”, Zhang et al 2024
  • “APISR: Anime Production Inspired Real-World Anime Super-Resolution”, Wang et al 2024
  • “Re:Draw—Context Aware Translation As a Controllable Method for Artistic Production”, Cardoso et al 2024
  • “MobileDiffusion: Subsecond Text-To-Image Generation on Mobile Devices”, Zhao et al 2023
  • “Adversarial Diffusion Distillation”, Sauer et al 2023
  • “UFOGen: You Forward Once Large Scale Text-To-Image Generation via Diffusion GANs”, Xu et al 2023
  • “Application of Generative Adversarial Networks in Color Art Image Shadow Generation”, Wu et al 2023
  • “Region Assisted Sketch Colorization”, Wang et al 2023e
  • “FlatGAN: A Holistic Approach for Robust Flat-Coloring in High-Definition With Understanding Line Discontinuity”, Kim et al 2023
  • “Consistency Trajectory Models (CTM): Learning Probability Flow ODE Trajectory of Diffusion”, Kim et al 2023
  • “The Colorization Based on Self-Attention Mechanism and GAN”, Sun et al 2023
  • “Generating Tabular Datasets under Differential Privacy”, Truda2023
  • “Semi-Supervised Reference-Based Sketch Extraction Using a Contrastive Learning Framework”, Seo et al 2023
  • “Semi-Implicit Denoising Diffusion Models (SIDDMs)”, Xu et al 2023
  • “StyleTTS 2: Towards Human-Level Text-To-Speech through Style Diffusion and Adversarial Training With Large Speech Language Models”, Li et al 2023
  • “High-Fidelity Audio Compression With Improved RVQGAN”, Kumar et al 2023
  • “Vocos: Closing the Gap between Time-Domain and Fourier-Based Neural Vocoders for High-Quality Audio Synthesis”, Siuzdak2023
  • “Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships”, Lan et al 2023
  • “TANGO: Text-To-Audio Generation Using Instruction-Tuned LLM and Latent Diffusion Model”, Ghosal et al 2023
  • “Thangka Sketch Colorization Based on Multi-Level Adaptive-Instance-Normalized Color Fusion and Skip Connection Attention”, Li et al 2023
  • “Two-Step Training: Adjustable Sketch Colorization via Reference Image and Text Tag”, Yan et al 2023
  • “Abstraction-Perception Preserving Cartoon Face Synthesis”, Ho et al 2023
  • “Approaching an Unknown Communication System by Latent Space Exploration and Causal Inference”, Beguš et al 2023
  • “GigaGAN: Scaling up GANs for Text-To-Image Synthesis”, Kang et al 2023
  • “Overview of Cartoon Face Generation”, Shen et al 2023
  • “Enhancing Image Representation in Conditional Image Synthesis”, Shim et al 2023
  • “StencilTorch: An Iterative and User-Guided Framework for Anime Lineart Colorization”, Hati et al 2023
  • “PMSGAN: Parallel Multistage GANs for Face Image Translation”, Liang et al 2023
  • “FAEC-GAN: An Unsupervised Face-To-Anime Translation Based on Edge Enhancement and Coordinate Attention”, Lin et al 2023
  • “A Survey on Text Generation Using Generative Adversarial Networks”, Rosa & Papa2022
  • “Appearance-Preserved Portrait-To-Anime Translation via Proxy-Guided Domain Adaptation”, Xiao et al 2022
  • “Seeing a Rose in 5,000 Ways”, Zhang et al 2022
  • “Reference Based Sketch Extraction via Attention Mechanism”, Ashtari et al 2022
  • “Dr.3D: Adapting 3D GANs to Artistic Drawings”, Jin et al 2022
  • “Null-Text Inversion for Editing Real Images Using Guided Diffusion Models”, Mokady et al 2022
  • “An Analysis: Different Methods about Line Art Colorization”, Gao et al 2022b
  • “Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization”, Cho et al 2022
  • “High Fidelity Neural Audio Compression”, Défossez et al 2022
  • “T2CI-GAN: Text to Compressed Image Generation Using Generative Adversarial Network”, Rajesh et al 2022
  • “GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images”, Gao et al 2022
  • “Musika! Fast Infinite Waveform Music Generation”, Pasini & Schlüter2022
  • “Using Generative Adversarial Networks for Conditional Creation of Anime Posters”, Sankalpa et al 2022
  • “AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos”, Wu et al 2022
  • “Learning to Generate Artistic Character Line Drawing”, Fang et al 2022
  • “Cascaded Video Generation for Videos In-The-Wild”, Castrejon et al 2022
  • “StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-To-Speech Synthesis”, Li et al 2022
  • “Why GANs Are Overkill for NLP”, Alvarez-Melis et al 2022
  • “VQGAN-CLIP: Open Domain Image Generation and Editing With Natural Language Guidance”, Crowson et al 2022
  • “Imitating, Fast and Slow: Robust Learning from Demonstrations via Decision-Time Planning”, Qi et al 2022
  • “TATS: Long Video Generation With Time-Agnostic VQGAN and Time-Sensitive Transformer”, Ge et al 2022
  • “MaxViT: Multi-Axis Vision Transformer”, Tu et al 2022
  • “Vector-Quantized Image Modeling With Improved VQGAN”, Yu et al 2022
  • “Truncated Diffusion Probabilistic Models and Diffusion-Based Adversarial Autoencoders”, Zheng et al 2022
  • “Do GANs Learn the Distribution? Some Theory and Empirics”, Arora et al 2022
  • “Using Constant Learning Rate of Two Time-Scale Update Rule for Training Generative Adversarial Networks”, Sato & Iiduka2022
  • “Microdosing: Knowledge Distillation for GAN Based Compression”, Helminger et al 2022
  • “An Unsupervised Font Style Transfer Model Based on Generative Adversarial Networks”, Zeng & Pan2021
  • “Multimodal Conditional Image Synthesis With Product-Of-Experts GANs”, Huang et al 2021
  • “TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems”, Doan et al 2021
  • “Compositional Transformers for Scene Generation”, Hudson & Zitnick2021
  • “Tackling the Generative Learning Trilemma With Denoising Diffusion GANs”, Xiao et al 2021
  • “EditGAN: High-Precision Semantic Image Editing”, Ling et al 2021
  • “Projected GANs Converge Faster”, Sauer et al 2021
  • “STransGAN: An Empirical Study on Transformer in GANs”, Xu et al 2021
  • “MSMT-GAN: Multi-Tailed, Multi-Headed, Spatial Dynamic Memory Refined Text-To-Image Synthesis”, Seshadri & Ravindran2021
  • “Unpaired Font Family Synthesis Using Conditional Generative Adversarial Networks”, Hassan et al 2021
  • “Fake It Till You Make It: Face Analysis in the Wild Using Synthetic Data Alone”, Wood et al 2021
  • “MCL-GAN: Generative Adversarial Networks With Multiple Specialized Discriminators”, Choi & Han2021
  • “ViTGAN: Training GANs With Vision Transformers”, Lee et al 2021
  • “MLP Singer: Towards Rapid Parallel Korean Singing Voice Synthesis”, Tae et al 2021
  • “HiT: Improved Transformer for High-Resolution GANs”, Zhao et al 2021
  • “GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (works for Videos Too!)”, Chong & Forsyth2021
  • “MixerGAN: An MLP-Based Architecture for Unpaired Image-To-Image Translation”, Cazenavette & Guevara2021
  • “EigenGAN: Layer-Wise Eigen-Learning for GANs”, He et al 2021
  • “Image Super-Resolution via Iterative Refinement”, Saharia et al 2021
  • “Deep Generative Modeling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models”, Bond-Taylor et al 2021
  • “AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation”, Li et al 2021
  • “Improved Denoising Diffusion Probabilistic Models”, Nichol & Dhariwal2021
  • “The Role of AI Attribution Knowledge in the Evaluation of Artwork”, Gangadharbatla2021
  • “XMC-GAN: Cross-Modal Contrastive Learning for Text-To-Image Generation”, Zhang et al 2021
  • “Stylized-Colorization for Line Arts”, Fang et al 2021
  • “Taming Transformers for High-Resolution Image Synthesis”, Esser et al 2020
  • “VQ-GAN: Taming Transformers for High-Resolution Image Synthesis”, Esser et al 2020
  • “LDM: Automatic Colorization of Anime Style Illustrations Using a Two-Stage Generator”, Lee & Lee2020
  • “DStyle-GAN: Generative Adversarial Network Based on Writing and Photography Styles for Drug Identification in Darknet Markets”, Zhang et al 2020
  • “Automatic Colorization of High-Resolution Animation Style Line-Art Based on Frequency Separation and Two-Stage Generator”, Lee & Lee2020b
  • “Image Generators With Conditionally-Independent Pixel Synthesis”, Anokhin et al 2020
  • “RetinaGAN: An Object-Aware Approach to Sim-To-Real Transfer”, Ho et al 2020
  • “Few-Shot Adaptation of Generative Adversarial Networks”, Robb et al 2020
  • “HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis”, Kong et al 2020
  • “A Good Image Generator Is What You Need for High-Resolution Video Synthesis”, Tian et al 2020
  • “Why Spectral Normalization Stabilizes GANs: Analysis and Improvements”, Lin et al 2020
  • “Denoising Diffusion Probabilistic Models”, Ho et al 2020
  • “Improving GAN Training With Probability Ratio Clipping and Sample Reweighting”, Wu et al 2020
  • “Object Segmentation Without Labels With Large-Scale Generative Models”, Voynov et al 2020
  • “Generative Adversarial Phonology: Modeling Unsupervised Phonetic and Phonological Learning With Neural Networks”, Beguš2020
  • “CiwGAN and FiwGAN: Encoding Information in Acoustic Data to Model Lexical Learning With Generative Adversarial Networks”, Beguš2020
  • “Learning to Simulate Dynamic Environments With GameGAN”, Kim et al 2020
  • “Reference-Based Sketch Image Colorization Using Augmented-Self Reference and Dense Semantic Correspondence”, Lee et al 2020
  • “Learning to Simulate Dynamic Environments With GameGAN [Homepage]”, Kim et al 2020
  • “MakeItTalk: Speaker-Aware Talking-Head Animation”, Zhou et al 2020
  • “Avatar Artist Using GAN [CS230]”, Su & Fang2020
  • “PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models”, Menon et al 2020
  • “Do We Need Zero Training Loss After Achieving Zero Training Error?”, Ishida et al 2020
  • “E621 Face Dataset”, Arfafax2020
  • “Smooth Markets: A Basic Mechanism for Organizing Gradient-Based Learners”, Balduzzi et al 2020
  • “MicrobatchGAN: Stimulating Diversity With Multi-Adversarial Discrimination”, Mordido et al 2020
  • “StarGAN Based Facial Expression Transfer for Anime Characters”, Mobini & Ghaderi2020
  • “Deep-Eyes: Fully Automatic Anime Character Colorization With Painting of Details on Empty Pupils”, Akita et al 2020
  • “Explorable Super Resolution”, Bahat & Michaeli2019
  • “PaintsTorch: a User-Guided Anime Line Art Colorization Tool With Double Generator Conditional Adversarial Network”, Hati et al 2019
  • “Generating Furry Face Art from Sketches Using a GAN”, Yu2019
  • “Interactive Anime Sketch Colorization With Style Consistency via a Deep Residual Neural Network”, Ye et al 2019
  • “Small-GAN: Speeding Up GAN Training Using Core-Sets”, Sinha et al 2019
  • “Parallel WaveGAN: A Fast Waveform Generation Model Based on Generative Adversarial Networks With Multi-Resolution Spectrogram”, Yamamoto et al 2019
  • “Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss”, Kim et al 2019
  • “Anime Sketch Coloring With Swish-Gated Residual U-Net and Spectrally Normalized GAN (SSN-GAN)”, Liu et al 2019
  • “The Generative Adversarial Brain”, Gershman2019
  • “Training Language GANs from Scratch”, d’Autume et al 2019
  • “Adversarial Examples Are Not Bugs, They Are Features”, Ilyas et al 2019
  • “Few-Shot Unsupervised Image-To-Image Translation”, Liu et al 2019
  • “COCO-GAN: Generation by Parts via Conditional Coordinating”, Lin et al 2019
  • “Compressing GANs Using Knowledge Distillation”, Aguinaldo et al 2019
  • “How AI Training Scales”, McCandlish et al 2018
  • “InGAN: Capturing and Remapping the “DNA” of a Natural Image”, Shocher et al 2018
  • “GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint”, Su2018
  • “Language GANs Falling Short”, Caccia et al 2018
  • “ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks”, Wang et al 2018
  • “Twin-GAN: Unpaired Cross-Domain Image Translation With Weight-Sharing GANs”, Li2018
  • “IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis”, Huang et al 2018
  • “Sem-GAN: Semantically-Consistent Image-To-Image Translation”, Cherian & Sullivan2018
  • “Cartoon Set”, Royer et al 2018
  • “The Relativistic Discriminator: a Key Element Missing from Standard GAN”, Jolicoeur-Martineau2018
  • “An Empirical Study on Evaluation Metrics of Generative Adversarial Networks”, Xu et al 2018
  • “Bidirectional Learning for Robust Neural Networks”, Pontes-Filho & Liwicki2018
  • “GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training”, Akcay et al 2018
  • “Toward Diverse Text Generation With Inverse Reinforcement Learning”, Shi et al 2018
  • “Synthesizing Programs for Images Using Reinforced Adversarial Learning”, Ganin et al 2018
  • “A Variational Inequality Perspective on Generative Adversarial Networks”, Gidel et al 2018
  • “ChatPainter: Improving Text to Image Generation Using Dialogue”, Sharma et al 2018
  • “Spectral Normalization for Generative Adversarial Networks”, Miyato et al 2018
  • “Unsupervised Cipher Cracking Using Discrete GANs”, Gomez et al 2018
  • “Two-Stage Sketch Colorization”, Zhang et al 2018b
  • “RenderGAN: Generating Realistic Labeled Data”, Sixt et al 2018
  • “CycleGAN, a Master of Steganography”, Chu et al 2017
  • “Multi-Content GAN for Few-Shot Font Style Transfer”, Azadi et al 2017
  • “High-Resolution Image Synthesis and Semantic Manipulation With Conditional GANs”, Wang et al 2017
  • “Are GANs Created Equal? A Large-Scale Study”, Lucic et al 2017
  • “AttnGAN: Fine-Grained Text to Image Generation With Attentional Generative Adversarial Networks”, Xu et al 2017
  • “StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-To-Image Translation”, Choi et al 2017
  • “Style Transfer in Text: Exploration and Evaluation”, Fu et al 2017
  • “XGAN: Unsupervised Image-To-Image Translation for Many-To-Many Mappings”, Royer et al 2017
  • “Mixed Precision Training”, Micikevicius et al 2017
  • “GraspGAN: Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping”, Bousmalis et al 2017
  • “OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning”, Henderson et al 2017
  • “Training Shallow and Thin Networks for Acceleration via Knowledge Distillation With Conditional Adversarial Networks”, Xu et al 2017
  • “PassGAN: A Deep Learning Approach for Password Guessing”, Bril et al 2017
  • “Towards the Automatic Anime Characters Creation With Generative Adversarial Networks”, Jin et al 2017
  • “Learning Universal Adversarial Perturbations With Generative Models”, Hayes & Danezis2017
  • “Semi-Supervised Haptic Material Recognition for Robots Using Generative Adversarial Networks”, Erickson et al 2017
  • “Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration”, Rahmatizadeh et al 2017
  • “CAN: Creative Adversarial Networks, Generating “Art” by Learning About Styles and Deviating from Style Norms”, Elgammal et al 2017
  • “Language Generation With Recurrent Generative Adversarial Networks without Pre-Training”, Press et al 2017
  • “Adversarial Ranking for Language Generation”, Lin et al 2017
  • “Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models”, Guimaraes et al 2017
  • “Stabilizing Training of Generative Adversarial Networks through Regularization”, Roth et al 2017
  • “SD-GAN: Semantically Decomposing the Latent Spaces of Generative Adversarial Networks”, Donahue et al 2017
  • “On Convergence and Stability of GANs”, Kodali et al 2017
  • “Accelerating Science With Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters”, Paganini et al 2017
  • “Outline Colorization through Tandem Adversarial Networks”, Frans2017
  • “Adversarial Neural Machine Translation”, Wu et al 2017
  • “Improved Training of Wasserstein GANs”, Gulrajani et al 2017
  • “CycleGAN: Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks”, Zhu et al 2017
  • “Mastering Sketching: Adversarial Augmentation for Structured Prediction”, Simo-Serra et al 2017
  • “I2T2I: Learning Text to Image Synthesis With Textual Data Augmentation”, Dong et al 2017
  • “Improving Neural Machine Translation With Conditional Sequence Generative Adversarial Nets”, Yang et al 2017
  • “Learning to Discover Cross-Domain Relations With Generative Adversarial Networks”, Kim et al 2017
  • “ArtGAN: Artwork Synthesis With Conditional Categorical GANs”, Tan et al 2017
  • “Wasserstein GAN”, Arjovsky et al 2017
  • “NIPS 2016 Tutorial: Generative Adversarial Networks”, Goodfellow2016
  • “Learning from Simulated and Unsupervised Images through Adversarial Training”, Shrivastava et al 2016
  • “Generative Adversarial Parallelization”, Im et al 2016
  • “Stacked Generative Adversarial Networks”, Huang et al 2016
  • “Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space”, Nguyen et al 2016
  • “Pix2Pix: Image-To-Image Translation With Conditional Adversarial Networks”, Isola et al 2016
  • “A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models”, Finn et al 2016
  • “Connecting Generative Adversarial Networks and Actor-Critic Methods”, Pfau & Vinyals2016
  • “Neural Photo Editing With Introspective Adversarial Networks”, Brock et al 2016
  • “SeqGAN: Sequence Generative Adversarial Nets With Policy Gradient”, Yu et al 2016
  • “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, Ledig et al 2016
  • “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets”, Chen et al 2016
  • “Generative Adversarial Imitation Learning”, Ho & Ermon2016
  • “Improved Techniques for Training GANs”, Salimans et al 2016
  • “Minibatch Discrimination”, Salimans et al 2016 (page 3 org openai)
  • “Adversarial Feature Learning”, Donahue et al 2016
  • “Generating Images With Recurrent Adversarial Networks”, Im et al 2016
  • “Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks”, Radford et al 2015
  • “Generative Adversarial Networks”, Goodfellow et al 2014
  • “Meta-Font, Metamathematics, and Metaphysics: Comments on Donald Knuth’s Article ‘The Concept of a Meta-Font’”, Hofstadter1982
  • “Introducing AuraSR—An Open Reproduction of the GigaGAN Upscaler”
  • “Generating Large Images from Latent Vectors”, Ha2024
  • “Learning to Write Programs That Generate Images”
  • “Deconvolution and Checkerboard Artifacts”
  • “TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up”
  • “Akanazawa/vgan: Code for Image Generation of Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow”
  • “Akanimax/Variational_Discriminator_Bottleneck: Implementation (with Some Experimentation) of the Paper Titled “Variational Discriminator Bottleneck””
  • “MSG-GAN: Multi-Scale Gradients GAN (Architecture Inspired from ProGAN but Doesn’t Use Layer-Wise Growing)”
  • “GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint”
  • “IntroVAE: A PyTorch Implementation of Paper ‘IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis’”
  • “Twin-GAN: Unpaired Cross-Domain Image Translation With Weight-Sharing GANs”
  • “Junyanz/CycleGAN: Software That Can Generate Photos from Paintings, Turn Horses into Zebras, Perform Style Transfer, and More.”
  • “Kevinlyu/DCGAN_Pytorch: DCGAN With Vanilla GAN and Least Square GAN Objective”
  • “Martinarjovsky/WassersteinGAN”
  • “Nolan-Dev/GANInterface: Tool to Interface With a StyleGAN Model”
  • “Learning to Simulate Dynamic Environments With GameGAN (CVPR 2020)”
  • “A Good Image Generator Is What You Need for High-Resolution Video Synthesis”
  • “Yasinyazici/EMA_GAN”
  • “Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks”
  • “Tour of the Sacred Library”
  • “Image Generation”
  • “Case Study: Interpreting, Manipulating, and Controlling CLIP With Sparse Autoencoders”
  • “Steganography and the CycleGAN—Alignment Failure Case Study”
  • “Welcome to Simulation City, the Virtual World Where Waymo Tests Its Autonomous Vehicles”
  • “The Rise of Anime Generating AI”
  • “Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow [Homepage]”
• Miscellaneous
• Bibliography